scholarly journals Dynamic behavior of the HTR-10 reactor: Dual temperature feedback model

2015 ◽  
Vol 30 (2) ◽  
pp. 124-131
Author(s):  
Seyed Hosseini
Keyword(s):  
Dynamic Behavior ◽  
Lower Sensitivity ◽  
Inherent Safety ◽  
Feedback Model ◽  
Temperature Feedback ◽  
Proposed Model ◽  
Reactivity Coefficients ◽  
Point Kinetics ◽  
Final System ◽  
Reactivity Insertion

The current work aims at presenting a simple model for PBM-type reactors' dynamic behavior analysis. The proposed model is based on point kinetics equations coupled with feedbacks from fuel and moderator temperatures. The temperature reactivity coefficients were obtained through MCNP code and via available experimental data. Parameters such as heat capacity and heat conductivity were carefully analyzed and the final system of equations was numerically solved. The obtained results, while in partial agreement with previously proposed models, suggest lower sensitivity to step reactivity insertion as compared to other reactor designs and inherent safety of the design.

scholarly journals A New Accurate Numerical Method Based on Shifted Chebyshev Series for Nuclear Reactor Dynamical Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Yasser Mohamed Hamada
Keyword(s):  
Nuclear Reactor ◽  
Approximate Solutions ◽  
Time Interval ◽  
Neutron Density ◽  
Chebyshev Series ◽  
System A ◽  
Temperature Feedback ◽  
Exponential Rate Of Convergence ◽  
Point Kinetics ◽  
The Given

A new method based on shifted Chebyshev series of the first kind is introduced to solve stiff linear/nonlinear systems of the point kinetics equations. The total time interval is divided into equal step sizes to provide approximate solutions. The approximate solutions require determination of the series coefficients at each step. These coefficients can be determined by equating the high derivatives of the Chebyshev series with those obtained by the given system. A new recurrence relation is introduced to determine the series coefficients. A special transformation is applied on the independent variable to map the classical range of the Chebyshev series from [-1,1] to [0,h]. The method deals with the Chebyshev series as a finite difference method not as a spectral method. Stability of the method is discussed and it has proved that the method has an exponential rate of convergence. The method is applied to solve different problems of the point kinetics equations including step, ramp, and sinusoidal reactivities. Also, when the reactivity is dependent on the neutron density and step insertion with Newtonian temperature feedback reactivity and thermal hydraulics feedback are tested. Comparisons with the analytical and numerical methods confirm the validity and accuracy of the method.

Parallel Computation of the Point Neutron Kinetic Equations Using Parallel Revisionist Integral Deferred Correction

2018 ◽  
Author(s):  
Yun Cai ◽  
Xingjie Peng ◽  
Qing Li ◽  
Zhizhu Zhang ◽  
Zhumin Jiang ◽  
...  
Keyword(s):  
Time Integration ◽  
Kinetic Equations ◽  
Accurate Method ◽  
Small Time ◽  
Time Step ◽  
Deferred Correction ◽  
Time Integration Method ◽  
Speed Up ◽  
Point Kinetics ◽  
Reactivity Insertion

The point kinetics is very important to the safety of the reactor operation. However, these equations are stiff and usually solved with very small time step. These equations are solved by Revisionist integral deferred correction (RIDC), which is a parallel time integration method. RIDC is a highly accurate method, and it reduces the error by iteration. Based on C++ and MPI, a four-core fourth-order RIDC is implemented and tested by several cases, such as step, ramp, and sinusoidal reactivity insertion. Compared with other methods, the time step of RIDC in the step reactivity insertion case is smaller, but it’s larger in the case of the sinusoidal reactivity insertion. RIDC can keep high accuracy while the time step is appropriately large. The numerical results also show that the speed-up ratio can achieve 2 when 4 processors are used.

scholarly journals A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
G. Kothai ◽  
E. Poovammal ◽  
Gaurav Dhiman ◽  
Kadiyala Ramana ◽  
Ashutosh Sharma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dynamic Behavior ◽  
Traffic Congestion ◽  
Performance Metrics ◽  
Learning Algorithm ◽  
Smart Cities ◽  
Experimental Result ◽  
Learning Models ◽  
Essential Information ◽  
Proposed Model

The vehicular adhoc network (VANET) is an emerging research topic in the intelligent transportation system that furnishes essential information to the vehicles in the network. Nearly 150 thousand people are affected by the road accidents that must be minimized, and improving safety is required in VANET. The prediction of traffic congestions plays a momentous role in minimizing accidents in roads and improving traffic management for people. However, the dynamic behavior of the vehicles in the network degrades the rendition of deep learning models in predicting the traffic congestion on roads. To overcome the congestion problem, this paper proposes a new hybrid boosted long short-term memory ensemble (BLSTME) and convolutional neural network (CNN) model that ensemble the powerful features of CNN with BLSTME to negotiate the dynamic behavior of the vehicle and to predict the congestion in traffic effectively on roads. The CNN extracts the features from traffic images, and the proposed BLSTME trains and strengthens the weak classifiers for the prediction of congestion. The proposed model is developed using Tensor flow python libraries and are tested in real traffic scenario simulated using SUMO and OMNeT++. The extensive experimentations are carried out, and the model is measured with the performance metrics likely prediction accuracy, precision, and recall. Thus, the experimental result shows 98% of accuracy, 96% of precision, and 94% of recall. The results complies that the proposed model clobbers the other existing algorithms by furnishing 10% higher than deep learning models in terms of stability and performance.

scholarly journals Comparative Analysis of Reactivity Calculations for the CERMET Fueled ADS with Serpent and MCNP6 Codes

2021 ◽  
Vol 37 (1) ◽  
Author(s):  
Vu Thanh Mai ◽  
Donny Hartanto ◽  
Pham Nhu Viet Ha ◽  
Nguyen Thi Dung ◽  
Bui Thi Hoa ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Comparative Analysis ◽  
Nuclear Waste ◽  
Inherent Safety ◽  
Fuel Temperature ◽  
Safety Feature ◽  
Temperature Feedback ◽  
Driven System ◽  
Accelerator Driven System ◽  
Good Agreement

The ADS (accelerator driven system) is recognized as a promising system to annihilate the radioactivity of nuclear waste with its inherent safety feature and waste transmutation potential. Thus, conceptual designs of ADS are widely carrying out. In order to verify the accuracy of an innovative ADS core modeling by using simulation codes, the reactivity calculations of CERMET fueled ADS were conducted using two Monte Carlo codes, Serpent and MCNP6 with ENDF/B-VII.0 library. The comparison shows a good agreement between two codes including the eigenvalue (less than 50 pcm) and fuel temperature feedback (discrepancy is within the standard deviation). It implies that the ADS was modelled successfully and can be used for further investigation.  Keywords: CERMET fueled ADS, Serpent code, MCNP6, reactivity calculation.

Online Adversarial Learning of Reactor State

2018 ◽  
Author(s):  
Yeni Li ◽  
Hany S. Abdel-Khalik ◽  
Elisa Bertino
Keyword(s):  
Accurate Estimation ◽  
Adversarial Learning ◽  
Small Perturbations ◽  
Raw Data ◽  
Defensive Strategies ◽  
Injection Attacks ◽  
Online Mode ◽  
Conditional Estimation ◽  
Reactivity Coefficients ◽  
Point Kinetics

This paper is in support of our recent efforts to designing intelligent defenses against false data injection attacks, where false data are injected in the raw data used to control the reactor. Adopting a game-model between the attacker and the defender, we focus here on how the attacker may estimate reactor state in order to inject an attack that can bypass normal reactor anomaly and outlier detection checks. This approach is essential to designing defensive strategies that can anticipate the attackers moves. More importantly, it is to alert the community that defensive methods based on approximate physics models could be bypassed by the attacker who can approximate the models in an online mode during a lie-in-wait period. For illustration, we employ a simplified point kinetics model and show how an attacker, once gaining access to the reactor raw data, i.e., instrumentation readings, can inject small perturbations to learn the reactor dynamic behavior. In our context, this equates to estimating the reactivity feedback coefficients, e.g., Doppler, Xenon poisoning, etc. We employ a non-parametric learning approach that employs alternating conditional estimation in conjunction with discrete Fourier transform and curve fitting techniques to estimate reactivity coefficients. An Iranian model of the Bushehr reactor is employed for demonstration. Results indicate that very accurate estimation of reactor state could be achieved using the proposed learning method.

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